1. Field of the Invention
The present invention relates to a method and system for the detection of microcalcifications in digital mammograms, and in particular to a method and system for the detection of clustered microcalcifications in digital mammograms using a shift-invariant neural network.
2. Discussion of the Background
Breast cancer causes 44,000 deaths per year in the United States. Mammography has been proven to be the primary diagnostic procedure for the early detection of breast cancer. Between 30% and 50% of breast carcinomas demonstrate microcalcifications upon histologic examination. Therefore, clustered microcalcifications in mammograms are an important sign in the detection of breast carcinoma. To assist radiologists in detecting clustered microcalcifications on mammograms, an automated computerized scheme based on filtering and feature extracting techniques has been developed as reported in Chan et al., "Improvement in Radiologist's Detection of Clustered Microcalcifications on Mammograms: The Potential of Computer-aided Diagnosis," Invest Radiol 25, 1102-10 (1990). The automated computer scheme identifies a small region of potential clustered microcalcifications, which is then indicated on the digitized mammogram. In an analysis of 78 mammograms, 85% of the clusters were detected with 1.5 false positive detections per image, which do not actually contain clustered microcalcifications.
It is generally desirable to improve the sensitivity of the computer-automated detection (CAD) scheme in order to detect the most subtle cases. However, as the sensitivity increases with this CAD scheme, the false-positive detection rate will also increase. To improve the overall performance, an artificial neural network has been applied to eliminate some of the false-positive detections indicated by the CAD scheme (see Wu et al., "Computerized Detection of Clustered Microcalcifications in Digital Mammograms: Applications of Neural Networks," Med. Phys. 19, 555-60 (1992). The neural network used in this study was a conventional three layer feed-forward neural network with a single output unit. The power spectra of the regions of interest (ROIs) indicated by the CAD scheme were used as the input of the neural network. The neural network was trained to classify positive or negative ROIs with its output value of 1 or 0, respectively. In the study discussed above, about 20% of the false-positive detections could be eliminated by the neural network without any loss of the positive detections.
Artificial neural networks have been shown to be a powerful tool for pattern recognition and data classification. The major difference between neural networks and conventional algorithmic approaches to information processing is that the problems are not solved by use of a predetermined algorithm, but rather by "training" using examples repeatedly. Therefore, the issue of generalization, whether training a neural network so that it would respond reasonably well to inputs not present in the training database, is very important (see, for example, Zhang et al. "Error Back Propagation with Minimum-entropy Weights: A Technique for Better Generalization of 2-D Shift-invariant NNs," Proc. Intl. Joint Conf. Neural Networks," Seattle 1991, I-212 through I-215). Without proper generalization, the potential advantage of neural networks will be limited, because one could simply use a look-up table to solve the problem. A neural network with high generalizing ability implies that one can obtain the high performance by training with a few training examples, as compared to that by a neural network with low generalizing ability. This is very important for application of neural networks to medical image processing and decision making, because usually the databases in medical applications are very large and the training of neural networks can be very time consuming.